At the end of this computer practical, students should be able to:
Before we begin
For this lab, you will need to:
Create a new Quarto document in your project folder (or create a new RStudio project) to simplify file management and reproducibility
Download the data files from the links provided in Exercise 1
Make sure you have the following packages installed and loaded:
tidyverse: For data manipulation and visualization (includes ggplot2)
moments: For calculating skewness and kurtosis
patchwork: For combining multiple plots
You can install packages using install.packages("package_name") if needed, and load them with library(package_name).
Exercise 1: Dataset exploration
Exploring the pie_crab dataset
In this lab, we’ll work with two environmental datasets. They can be downloaded from the links below:
pie_crab.csv: Crab size measurements across different sites and latitudes
hbr_maples.csv: Maple seedling measurements from different watersheds
By now, you should know how to use read_csv() to read these datasets into R. If you need a refresher, refer to the previous labs or ask for help from a demonstrator (if available).
Let’s start by exploring the pie_crab dataset. We’ve done this before, but it’s always good to refresh our memory as these functions are extremely common to use.
The str() function shows us the structure of the dataset, including variable names, types, and a preview of the values.
The head() function shows the first six rows of the dataset, giving us a quick look at the data.
The summary() function provides descriptive statistics for each variable, including minimum, maximum, mean, and quartiles for numeric variables.
date latitude site size
Min. :2016-07-24 Min. :30.00 Length:392 Min. : 6.64
1st Qu.:2016-07-28 1st Qu.:34.00 Class :character 1st Qu.:12.02
Median :2016-08-01 Median :39.10 Mode :character Median :14.44
Mean :2016-08-02 Mean :37.69 Mean :14.66
3rd Qu.:2016-08-09 3rd Qu.:41.60 3rd Qu.:17.34
Max. :2016-08-13 Max. :42.70 Max. :23.43
air_temp air_temp_sd water_temp water_temp_sd
Min. :10.29 Min. :6.391 Min. :13.98 Min. :4.838
1st Qu.:12.05 1st Qu.:8.110 1st Qu.:14.33 1st Qu.:6.567
Median :13.93 Median :8.410 Median :17.50 Median :6.998
Mean :15.20 Mean :8.654 Mean :17.65 Mean :7.252
3rd Qu.:18.63 3rd Qu.:9.483 3rd Qu.:20.54 3rd Qu.:7.865
Max. :21.79 Max. :9.965 Max. :24.50 Max. :9.121
name
Length:392
Class :character
Mode :character
Question 1
What variables are in the dataset? What are the data types of each variable? Are there any missing values? What are the ranges of the numeric variables?
Answer 1
From our exploration of the pie_crab dataset, we can see that:
What variables are in the dataset? The dataset contains 9 variables: date, latitude, site, size, air_temp, air_temp_sd, water_temp, water_temp_sd, and name
What are the data types of each variable? Data types:
site, name: character variables (which we’ll later convert to factors)
Are there any missing values? Based on the summary output there are no missing values
What are the ranges of the numeric variables?
# We can answer this question by viewing the numbers in `summmary()`# but, we can compute it too. See ?sapply for more informationsapply(pie_crab[, sapply(pie_crab, is.numeric)], range)
From our exploration, we can see that the pie_crab dataset contains information about:
date: When the crabs were measured
latitude: The latitude where the crabs were collected
site: The specific collection site
size: The size of the crabs in millimeters
air_temp: Air temperature in degrees Celsius
water_temp: Water temperature in degrees Celsius
Identifying factors in the data
One of the first steps in data exploration is to determine if your data types are recognised correctly by R. Looking at the output of str() can help you identify variables that might be better represented as different data types.
In our dataset, site and name are character variables with repeating values. When categorical variables like these have a limited number of possible values that repeat throughout the dataset, they’re good candidates to be converted to factors. Factors are R’s way of representing categorical data efficiently.
We can check how many unique values these variables have using the unique() function:
[1] "Guana Tolomoto Matanzas NERR" "Sapelo Island NERR"
[3] "North Inlet Winyah Bay NERR" "Zeke's Island NERR"
[5] "Rachel Carson NERR" "Virginia Coastal Reserve LTER"
[7] "Delaware Bay NERR" "Jacques Cousteau NERR"
[9] "Sixpenny Island - Connecticut" "Narragansett Bay NERR"
[11] "Cape Cod" "Bare Cove Park"
[13] "Plum Island Estuary - West Creek"
For a dataset with 392 observations, having only a few unique sites and names suggests that these variables should be factors. Let’s count exactly how many unique values we have:
length(unique(pie_crab$site))
[1] 13
length(unique(pie_crab$name))
[1] 13
We can convert these character variables to factors using the factor() function:
# Convert site and name to factorspie_crab$site <-factor(pie_crab$site)pie_crab$name <-factor(pie_crab$name)# Check the structure again to confirm the conversionstr(pie_crab)
Notice how we’ve converted the specific variables to factors and used the assignment operator <- to update the dataset. This is a common pattern in R, where we update the dataset in place. The output of str() now shows these variables as factors with their levels (unique values) listed.
Converting to factors is important because:
It makes R treat the data appropriately in statistical analyses
It can make visualizations more informative
It improves memory efficiency for large datasets
Exercise 2: Building visualizations with ggplot2
The grammar of graphics
The ggplot2 package is based on the “grammar of graphics,” a framework that breaks visualisations into components, similar to how grammar breaks language into parts of speech. This approach makes it possible to create complex visualisations by combining simple elements.
The key components (or layers) include:
Data: The dataset being visualised
Aesthetics: Mappings from data variables to visual properties
Geometries: The shapes used to represent the data
Facets: Subplots that show different subsets of the data
Statistics: Transformations of the data (e.g., counts, means)
Coordinates: The space in which the data is plotted
Themes: Visual styling of non-data elements
Let’s learn how to create visualisations using this approach by building a plot step by step, explaining each component along the way. As you follow along, you are improving the code that produces the plot – not re-writing it – at each step.
Step 1: The canvas
Every ggplot2 visualisation starts with a blank canvas:
# Start with an empty canvasggplot()
This creates an empty plotting space. It doesn’t show anything yet because we haven’t specified any data or how to visualise it.
Step 2: Adding data
Next, we tell ggplot2 what data to use. Ideally the data is a data.frame or tibble object (which you will know by using str() previously):
# Add data to the plotggplot(data = pie_crab)
We’ve now told ggplot2 to use the pie_crab dataset, but we still cannot see anything because we have not specified which variables to plot – or how to represent them.
Step 3: Mapping aesthetics
Aesthetics map variables in your data to visual properties in the plot. They are a function aes() on their own. Think of aesthetics as how you woul define x, y and other dimensions in the plot.
Here, we’ve mapped the size variable to the x-axis. This variable is something that exists in the pie_crab data frame. We still cannot see any data points because we haven’t specified how to represent the data (e.g., as points, bars, or lines).
Step 4: Adding a Geometry
Geometries determine how the data is represented visually. They are functions that are prefixed by geom_* so that users know what they are meant to do. In most cases it is clear what type of plot is being create by reading the name of the geometry function(s) in the plot code.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Now we can see the data! We’ve added a histogram geometry (geom_histogram()), which counts the number of observations falling into bins along the x-axis. The + operator adds layers to the plot.
Notice the message about the default bin width. ggplot2 automatically chose 30 bins, but we can adjust this.
Step 5: Customizing the Geometry
Let’s customize our histogram:
# Customize the histogramggplot(data = pie_crab, mapping =aes(x = size)) +geom_histogram(bins =20, fill ="skyblue", color ="black")
We’ve made several changes:
bins = 20: Changed the number of bins to 20
fill = "skyblue": Set the fill color of the bars to sky blue
color = "black": Set the outline color of the bars to black
These are fixed properties applied to all bars, not mappings from data variables. For colours you may search “r colours” in your web browser to see what available colours you can use (it’s a lot).
Step 6: Adding Labels and Titles
Good visualisations have clear labels. Titles are optional – but the option is available if you need it. We can add all of these in the next layer using the labs() function:
# Add informative labelsggplot(data = pie_crab, mapping =aes(x = size)) +geom_histogram(bins =20, fill ="skyblue", color ="black") +labs(title ="Distribution of Crab Sizes",x ="Size (mm)",y ="Count" )
The labs() function adds various text elements to the plot:
title: The main title of the plot
x: The x-axis label
y: The y-axis label
Step 7: Applying a Theme
Themes control the overall appearance of the plot:
# Add a theme for consistent stylingggplot(data = pie_crab, mapping =aes(x = size)) +geom_histogram(bins =20, fill ="skyblue", color ="black") +labs(title ="Distribution of Crab Sizes",x ="Size (mm)",y ="Count" ) +theme_minimal()
The theme_minimal() function applies a minimalist theme with a white background and subtle grid lines. Other common themes include:
theme_classic(): No grid lines, simple axes
theme_light(): Light background with subtle grid lines
theme_dark(): Dark background for presentations
Bonus: Adding multiple geometries
One of the powerful features of ggplot2 is the ability to layer multiple geometries:
# Add a density curve on top of the histogramggplot(data = pie_crab, mapping =aes(x = size)) +geom_histogram(aes(y =after_stat(density)),bins =20,fill ="skyblue", color ="black" ) +geom_density(color ="red", linewidth =1) +labs(title ="Distribution of Crab Sizes",x ="Size (mm)",y ="Density" ) +theme_minimal()
In this plot:
We’ve changed the y-axis of the histogram to show density instead of count using aes(y = after_stat(density))
We’ve added a density curve with geom_density()
We’ve set the density curve color to red and increased its line width
Question 2
What’s the difference between setting a fixed property (like fill = "blue") and mapping a variable to an aesthetic (like aes(fill = site))?
How would you modify the histogram to have more or fewer bins? Use ?geom_histogram to help you think of an answer.
What would happen if you changed the order of the geom_histogram() and geom_density() layers?
These questions can typically be answered by making changes to the code to view the differences.
Answer 2
Fixed property vs. aesthetic mapping:
Fixed property (e.g., fill = "blue"): Applies the same value to all elements, regardless of data values
Aesthetic mapping (e.g., aes(fill = site)): Maps a variable in your data to a visual property, creating different values based on the data
Fixed properties are used for consistent styling, while aesthetic mappings are used to represent data values visually
Modifying histogram bins:
To change the number of bins: Use the bins parameter (e.g., geom_histogram(bins = 30))
To specify bin width directly: Use the binwidth parameter (e.g., geom_histogram(binwidth = 2))
Fewer bins show broader patterns but less detail, while more bins show more detail but may be noisier
Changing layer order:
If geom_density() came before geom_histogram(), the histogram would be drawn on top of the density curve
This would make the density curve partially or completely hidden behind the histogram
Layer order matters because layers are drawn in the order they are added, with later layers appearing on top
Exercise 3: Analysing environmental variables
Now that we understand the grammar of graphics approach, let’s analyse a different variable in our dataset.
Examining water temperature distribution
Let’s examine the distribution of water temperatures across our sampling sites:
# Create a basic histogram of water temperaturesggplot(pie_crab, aes(x = water_temp)) +geom_histogram(bins =15) +labs(title ="Distribution of Water Temperatures",x ="Water Temperature (°C)",y ="Count" )
The histogram shows us the frequency distribution of water temperatures. We can see the shape of the distribution, including any skewness or unusual patterns.
# Add a density curveggplot(pie_crab, aes(x = water_temp)) +geom_histogram(aes(y =after_stat(density)),bins =15,fill ="lightblue", colour ="black" ) +geom_density(colour ="red") +labs(title ="Distribution of Water Temperatures",x ="Water Temperature (°C)",y ="Density" )
Adding a density curve helps us see the overall shape of the distribution more clearly.
Question 3
What is the shape of the distribution of water temperatures? Does the distribution appear to be normal? Are there any outliers?
Answer 3
Based on the histogram and density plot of water temperatures:
Shape of the distribution: The distribution appears to be right-skewed (positively skewed), with a longer tail extending toward higher temperatures. The data also appears to be bimodal, although there are gaps that make it a bit harder to assert tha this is the case.
Normality: The distribution does not appear to be perfectly normal. A normal distribution would be symmetric around the mean, but this distribution shows some asymmetry. The density curve helps visualize this deviation from normality.
Outliers: There appear to be a few potential outliers on the right side of the distribution, representing unusually warm water temperatures. Outliers are (often) isolated bars in the histogram at the right end of the distribution.
Skewness and Kurtosis
To quantify the shape of the water temperature distribution, we can calculate skewness and kurtosis:
# Calculate skewness and kurtosis for water temperatureskewness_value <-skewness(pie_crab$water_temp)kurtosis_value <-kurtosis(pie_crab$water_temp)# Print the valuescat("Skewness of water temperature:", skewness_value, "\n")
Skewness of water temperature: 0.4750277
cat("Kurtosis of water temperature:", kurtosis_value, "\n")
Kurtosis of water temperature: 1.888369
Interpreting these values:
Skewness measures the asymmetry of the distribution:
0 = symmetric (like a normal distribution)
More than, > 0 = right-skewed (tail extends to the right)
Less than, < 0 = left-skewed (tail extends to the left)
Kurtosis measures the “tailedness” of the distribution:
3 = normal distribution (in the moments package, this is sometimes normalized to 0)
More than, > 3 = leptokurtic (heavy-tailed, more outliers)
Less than, < 3 = platykurtic (light-tailed, fewer outliers)
The skewness value of approximately 0.5 confirms our visual observation that the water temperature distribution is moderately right-skewed. The kurtosis value of approximately 2.5 indicates the distribution has slightly lighter tails than a normal distribution.
These numerical measures help us quantify what we observe visually in the histograms and density plots. Now that we understand the overall distribution of our data, let’s explore how it varies across different groups.
Exercise 4: Comparing groups
Now that we’ve examined the overall distribution of crab sizes, let’s compare sizes across different groups.
Creating boxplots to compare sites
Boxplots are excellent for comparing distributions between two or more groups:
# Create boxplots of crab sizes by siteggplot(pie_crab, aes(x = site, y = size)) +geom_boxplot(fill ="skyblue") +labs(title ="Crab Sizes by Site",x ="Site",y ="Size (mm)" )
A boxplot shows:
The median (middle line)
The interquartile range (IQR) from the 25th to 75th percentile (the box)
The whiskers (typically extend to 1.5 × IQR)
Outliers (points beyond the whiskers)
To see the actual data points alongside the boxplots we can add another geometric layer. You can see how it may or may not be useful to readers. In most cases adding the data points is not necessary, but in some cases it could be useful in the exploration phase (i.e. not the final plot for publication).
# Add points to see the actual dataggplot(pie_crab, aes(x = site, y = size)) +geom_boxplot(fill ="skyblue", alpha =0.5) +geom_jitter(width =0.2, alpha =0.5) +labs(title ="Crab Sizes by Site",x ="Site",y ="Size (mm)" )
We’ve added:
geom_jitter() to add individual data points with a slight horizontal jitter to avoid overplotting
alpha = 0.5 to make both the boxplots and points semi-transparent
width = 0.2 to control the amount of horizontal jittering
Question 4
How do crab sizes vary across different sites? Which site has the largest median crab size? Which site shows the most variability in crab sizes? Are there any outliers at specific sites? Answer these questions in a descriptive manner using the plot.
Here we are exercising our ability to see patterns from data visualisations and using them to make certain observations about the data.
Answer 4
Based on the boxplots comparing crab sizes across different sites:
Variation across sites: There is considerable variation in crab sizes between sites.
Largest median size: PIE (Plum Island Estuary) has the largest median crab size since the median line is the highest among the different boxplots.
Most variability: BC (Bare Cove Park) shows the most variability in crab sizes. This is evidenced by its wider box and longer whiskers in the boxplot, indicating a broader spread of sizes.
Outliers: Yes, there are outliers at specific sites. Several sites show individual points above or below their whiskers, representing unusually large or small crabs for those locations. These outliers are important to note as they may represent unique environmental conditions or genetic factors affecting growth.
Exploring the relationship between latitude and size
Let’s examine if there’s a relationship between latitude and crab size:
# Create a scatterplot of size vs. latitudeggplot(pie_crab, aes(x = latitude, y = size)) +geom_point(alpha =0.5) +labs(title ="Crab Size vs. Latitude",x ="Latitude",y ="Size (mm)" )
Scatterplots show the relationship between two continuous variables. Each point represents a single observation.
To help visualise the trend, we can add a trend line (something that we will cover in greater detail once we look at linear models in Week 7):
# Add a trend lineggplot(pie_crab, aes(x = latitude, y = size)) +geom_point(alpha =0.5) +geom_smooth(method ="lm", se =TRUE) +labs(title ="Crab Size vs. Latitude",x ="Latitude",y ="Size (mm)" )
`geom_smooth()` using formula = 'y ~ x'
We’ve added:
geom_smooth(method = "lm") to add a linear regression line
se = TRUE to include the standard error as a shaded confidence band
Question 5
Is there a relationship between latitude and crab size?
Answer 5
Based on the scatterplot it looks like as latitude increases, size also increases, because the trend line shows a general positive trend.
Exercise 5: Faceting and grouping
So far, we’ve created separate plots for different analyses. Now, let’s explore techniques for comparing multiple groups or variables within a single plot.
Exploring the hbr_maples dataset
Let’s switch to our second dataset, which contains measurements of maple seedlings from different watersheds:
# Examine the structure of the maples datasetstr(hbr_maples)
# Get a summary of the variablessummary(hbr_maples)
year watershed elevation transect sample
Min. :2003 Reference:179 Low :120 R1 : 40 1 : 18
1st Qu.:2003 W1 :180 Mid :120 R2 : 40 2 : 18
Median :2003 NA's:119 W1-1 : 40 3 : 18
Mean :2003 W1-2 : 40 4 : 18
3rd Qu.:2004 W1-3 : 40 5 : 18
Max. :2004 R3 : 39 6 : 18
(Other):120 (Other):251
stem_length leaf1area leaf2area leaf_dry_mass
Min. : 52.70 Min. : 2.480 Min. : 3.40 Min. :0.01170
1st Qu.: 75.65 1st Qu.: 8.818 1st Qu.: 8.95 1st Qu.:0.03975
Median : 85.70 Median :11.636 Median :11.28 Median :0.05590
Mean : 86.86 Mean :11.800 Mean :11.75 Mean :0.06368
3rd Qu.: 97.05 3rd Qu.:14.016 3rd Qu.:14.30 3rd Qu.:0.07855
Max. :132.30 Max. :26.952 Max. :25.79 Max. :0.38700
NA's :119 NA's :119
stem_dry_mass corrected_leaf_area
Min. :0.00790 Min. : 9.597
1st Qu.:0.02360 1st Qu.:21.180
Median :0.03320 Median :25.950
Mean :0.04506 Mean :26.687
3rd Qu.:0.05820 3rd Qu.:31.751
Max. :0.15000 Max. :55.874
NA's :119
Now, let’s create histograms of stem length by watershed using faceting:
# Create histograms of stem length by watershedggplot(hbr_maples, aes(x = stem_length)) +geom_histogram(bins =20, fill ="forestgreen", colour ="black") +facet_wrap(~watershed) +labs(title ="Distribution of Stem Lengths by Watershed",x ="Stem Length (mm)",y ="Count" )
The facet_wrap() function creates separate panels for each value of the specified variable. This allows us to compare distributions across groups.
Question 6
How do stem lengths differ between watersheds? Which watershed shows more variability in stem lengths? Are the distributions similarly shaped? Use only the visualisations to answer these questions.
Answer 6
TBC
Comparing leaf area and stem length
Let’s examine the relationship between leaf area and stem length, comparing across watersheds:
# Create a scatterplot of leaf area vs. stem length, coloured by watershedggplot(hbr_maples, aes(x = stem_length, y = corrected_leaf_area, colour = watershed)) +geom_point(alpha =0.7) +labs(title ="Leaf Area vs. Stem Length",x ="Stem Length (mm)",y ="Corrected Leaf Area (cm²)",colour ="Watershed" )
Warning: Removed 119 rows containing missing values or values outside the scale range
(`geom_point()`).
Here, we’ve mapped the watershed variable to the colour aesthetic, which automatically creates a color-coded legend.
Let’s add separate trend lines for each watershed:
# Add separate trend lines for each watershedggplot(hbr_maples, aes(x = stem_length, y = corrected_leaf_area, colour = watershed)) +geom_point(alpha =0.7) +geom_smooth(method ="lm", se =FALSE) +labs(title ="Leaf Area vs. Stem Length",x ="Stem Length (mm)",y ="Corrected Leaf Area (cm²)",colour ="Watershed" )
`geom_smooth()` using formula = 'y ~ x'
Warning: Removed 119 rows containing non-finite outside the scale range
(`stat_smooth()`).
Warning: Removed 119 rows containing missing values or values outside the scale range
(`geom_point()`).
When we include colour = watershed in the global aesthetics, ggplot2 automatically applies this grouping to all geometries, including geom_smooth(). This creates separate trend lines for each watershed.
Question 7
Is there a relationship between stem length and leaf area? Does this relationship differ between watersheds? What might explain these differences from an ecological perspective?
Answer 7
Based on the scatterplot examining the relationship between stem length and leaf area:
Relationship existence: Yes, there is a clear positive relationship between stem length and leaf area. As stem length increases, leaf area tends to increase as well. This suggests that larger seedlings generally have both longer stems and larger leaves.
Differences between watersheds: The relationship does differ between watersheds. The trend lines for each watershed have different slopes, indicating that the rate at which leaf area increases with stem length varies. Watershed 6 appears to have a steeper slope than Watershed 1, suggesting that for each unit increase in stem length, seedlings in Watershed 6 gain more leaf area than those in Watershed 1.
Ecological explanation: These differences could be explained by several ecological factors:
Resource availability: Watershed 6 may have more favorable growing conditions (e.g., better soil nutrients, more water, or optimal light conditions) that allow seedlings to allocate more resources to leaf production relative to stem growth.
Adaptation to local conditions: Seedlings in different watersheds may have adapted different growth strategies in response to local environmental pressures.
Competition: Different levels of competition in each watershed might influence how seedlings allocate resources between stem and leaf growth.
Genetic differences: There could be genetic differences between maple populations in different watersheds that influence their growth patterns.
These findings highlight the importance of considering environmental context when studying plant growth relationships, as the same species can show different growth patterns in different habitats.
Exercise 6: Bonus take-home
These exercises are designed for you to practice the visualisation techniques we’ve covered in this lab. You can complete them during the lab if you finish early, or at home for additional practice.
Basic visualisation practice
Create a histogram of the water_temp variable in the crabs dataset. Calculate and interpret its skewness and kurtosis.
Create boxplots comparing the leaf_dry_mass between watersheds in the maples dataset. What do you observe?
Create a scatterplot examining the relationship between stem_dry_mass and leaf_dry_mass in the maples dataset, with points coloured by watershed.
Advanced challenge: patchwork
The patchwork package allows you to combine multiple plots into a single figure. This is useful for creating complex visualisations that tell a story about your data.
# Load the patchwork packagelibrary(patchwork)
Let’s create a few plots and then combine them:
# Create multiple plotsp1 <-ggplot(pie_crab, aes(x = size)) +geom_histogram(fill ="skyblue", colour ="black") +labs(title ="Distribution of Crab Sizes")p2 <-ggplot(pie_crab, aes(x = latitude, y = size)) +geom_point() +geom_smooth(method ="lm") +labs(title ="Size vs. Latitude")p3 <-ggplot(pie_crab, aes(x = site, y = size)) +geom_boxplot(fill ="skyblue") +labs(title ="Size by Site")# Combine plots (this is patchwork in action!)p1 / (p2 + p3)
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`geom_smooth()` using formula = 'y ~ x'
The patchwork syntax is intuitive:
/ arranges plots vertically (one above the other)
+ arranges plots horizontally (side by side)
You can use parentheses to control the layout
Now, try these exercises:
Create a combined plot using patchwork that shows:
A histogram of stem lengths
A scatterplot of stem length vs. leaf area
Boxplots of stem lengths by watershed
Arrange these plots in a 2x2 grid
Create a combined plot that tells a story about the relationship between temperature and crab size:
A scatterplot of air temperature vs. crab size
A scatterplot of water temperature vs. crab size
A boxplot of crab sizes by site
Arrange the scatterplots side by side and the boxplot below them
Solutions to Take-home Exercises
Histogram of water temperature with skewness and kurtosis:
# Create histogram of water temperatureggplot(pie_crab, aes(x = water_temp)) +geom_histogram(bins =15, fill ="lightblue", color ="black") +labs(title ="Distribution of Water Temperature",x ="Water Temperature (°C)",y ="Count" )
# Calculate skewness and kurtosisskew_water <-skewness(pie_crab$water_temp)kurt_water <-kurtosis(pie_crab$water_temp)cat("Skewness of water temperature:", skew_water, "\n")
Skewness of water temperature: 0.4750277
cat("Kurtosis of water temperature:", kurt_water, "\n")
Kurtosis of water temperature: 1.888369
Interpretation: The water temperature distribution has a skewness of approximately 0.5, indicating it is right-skewed (though only moderately). The kurtosis value of approximately 1.89 suggests the distribution is slightly platykurtic (light-tailed) compared to a normal distribution.
Boxplots of leaf dry mass by watershed:
# Create boxplots of leaf dry mass by watershedggplot(hbr_maples, aes(x = watershed, y = leaf_dry_mass)) +geom_boxplot(fill ="lightgreen") +labs(title ="Leaf Dry Mass by Watershed",x ="Watershed",y ="Leaf Dry Mass (g)" )
Observation: The boxplots show that Watershed 1 has higher median leaf dry mass compared to the reference.
Scatterplot of stem dry mass vs. leaf dry mass:
# Create scatterplot of stem dry mass vs. leaf dry massggplot(hbr_maples, aes(x = stem_dry_mass, y = leaf_dry_mass, color = watershed)) +geom_point(alpha =0.7) +geom_smooth(method ="lm", se =FALSE) +labs(title ="Relationship Between Stem and Leaf Dry Mass",x ="Stem Dry Mass (g)",y ="Leaf Dry Mass (g)",color ="Watershed" )
`geom_smooth()` using formula = 'y ~ x'
Observation: There appears to be a strong positive relationship between stem dry mass and leaf dry mass, suggesting that seedlings with larger stems also have more leaf mass.
Combined plot of stem length visualisations:
# Create individual plotsp1 <-ggplot(hbr_maples, aes(x = stem_length)) +geom_histogram(fill ="forestgreen", color ="black") +labs(title ="Distribution of Stem Lengths")p2 <-ggplot(hbr_maples, aes(x = stem_length, y = corrected_leaf_area)) +geom_point() +geom_smooth(method ="lm") +labs(title ="Stem Length vs. Leaf Area")p3 <-ggplot(hbr_maples, aes(x = watershed, y = stem_length)) +geom_boxplot(fill ="forestgreen") +labs(title ="Stem Length by Watershed")# Combine the plotsp1 / (p2 + p3)
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`geom_smooth()` using formula = 'y ~ x'
Warning: Removed 119 rows containing non-finite outside the scale range
(`stat_smooth()`).
Warning: Removed 119 rows containing missing values or values outside the scale range
(`geom_point()`).
Combined plot of temperature and crab size:
# Create individual plotsp1 <-ggplot(pie_crab, aes(x = air_temp, y = size)) +geom_point() +geom_smooth(method ="lm") +labs(title ="Size vs. Air Temperature")p2 <-ggplot(pie_crab, aes(x = water_temp, y = size)) +geom_point() +geom_smooth(method ="lm") +labs(title ="Size vs. Water Temperature")p3 <-ggplot(pie_crab, aes(x = site, y = size)) +geom_boxplot(fill ="skyblue") +labs(title ="Size by Site")# Combine the plots(p1 + p2) / p3
`geom_smooth()` using formula = 'y ~ x'
`geom_smooth()` using formula = 'y ~ x'
Summary
In this lab, we’ve explored how to create and customize various types of visualisations using ggplot2. We’ve learned:
The grammar of graphics approach to building visualisations layer by layer
How to create and interpret histograms, density plots, boxplots, and scatterplots
How to quantify and interpret distribution properties like skewness and kurtosis
How to compare groups using boxplots and faceting
How to examine relationships between variables using scatterplots and trend lines
How to combine multiple plots using the patchwork package
These skills will be valuable for exploring and presenting data in future labs and assignments.
